# EstimatedPointProcess

EstimatedPointProcess[pdata,pproc]

estimates the parametric point process pproc from point data pdata.

EstimatedPointProcess[pdata,pproc,{{p,p0},{q,q0},}]

estimates the parameters p, q, with starting values p0, q0, .

# Details and Options

• EstimatedPointProcess takes point data pdata and returns the symbolic point process pproc with parameter estimates inserted for any non-numeric values.
•
• In general, a process pproc can be better estimated from an ensemble of point data.
•
• The points pdata can have the following forms:
•  {p1,p2,…} points pi GeoPosition[…],GeoPositionXYZ[…],… geographic points SpatialPointData[…] spatial point collection {pts,reg} point collection pts and observation region reg
• The points are converted to a SpatialPointData object and a RipleyRasson estimator is used to generate the observation region if it is not provided in pdata.
• The following options can be given:
•  AccuracyGoal Automatic the accuracy sought PointProcessEstimator Automatic what process parameter estimator to use PrecisionGoal Automatic the precision sought WorkingPrecision Automatic the precision used in internal computations
• Settings for PointProcessEstimator are documented under the individual point process reference pages.

# Examples

open allclose all

## Basic Examples(1)

Estimate the parameter of a PoissonPointProcess:

Compare the nearest neighbor function of the estimated process to the original data:

## Scope(3)

#### Cluster point processes(1)

Simulate a point configuration from a Matern point process:

Use the "FindClusters" method to estimate a point process model:

Compare the Ripley measure between the original process and the estimated model:

#### Gibbs point processes(2)

Estimate a hardcore point process:

Use automatic method:

Estimate an interaction point process:

Estimate the point process:

## Options(3)

### PointProcessEstimator(2)

Estimate a cluster point process:

Use the "FindClusters" method to estimate a point process model:

Use the method of moments:

Estimate an interaction process:

Use "MaximumPseudoLikelihood" method:

Use "MaximumLikelihood" method:

### WorkingPrecision(1)

Estimate a cluster point process with arbitrary precision:

Specify WorkingPrecision:

EstimatedPointProcess uses MachinePrecision as default:

Wolfram Research (2020), EstimatedPointProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/EstimatedPointProcess.html.

#### Text

Wolfram Research (2020), EstimatedPointProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/EstimatedPointProcess.html.

#### CMS

Wolfram Language. 2020. "EstimatedPointProcess." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/EstimatedPointProcess.html.

#### APA

Wolfram Language. (2020). EstimatedPointProcess. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/EstimatedPointProcess.html

#### BibTeX

@misc{reference.wolfram_2023_estimatedpointprocess, author="Wolfram Research", title="{EstimatedPointProcess}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/EstimatedPointProcess.html}", note=[Accessed: 16-April-2024 ]}

#### BibLaTeX

@online{reference.wolfram_2023_estimatedpointprocess, organization={Wolfram Research}, title={EstimatedPointProcess}, year={2020}, url={https://reference.wolfram.com/language/ref/EstimatedPointProcess.html}, note=[Accessed: 16-April-2024 ]}